DataEase

DataEase is an open-source business intelligence platform built for non-technical users who need to analyze data without writing SQL or code. Its drag-and-drop interface lets anyone connect to databases, build interactive dashboards, and share insights securely across teams. By eliminating the dependency on IT departments for routine analytics, DataEase empowers business users, operations staff, and executives to derive insights from data on their own timeline.

The platform supports over 20 data source types spanning OLTP databases like MySQL, Oracle, SQL Server, and PostgreSQL, OLAP engines including ClickHouse, Apache Doris, and StarRocks, data warehouses like Amazon Redshift, and file formats such as Excel and CSV. Users can also connect API data sources for real-time feeds. With 200+ industry-specific dashboard templates covering manufacturing, e-commerce, finance, healthcare, and transportation, teams can deploy production-ready analytics in minutes rather than weeks.

DataEase integrates AI-powered natural language querying through its SQLBot module, which converts plain-language questions into SQL and renders the results as visualizations. Embedding options via iframe, REST API, and SDK allow SaaS products and enterprise applications to incorporate self-service analytics directly into their own interfaces. The platform ships with granular permission controls, data masking, password-protected share links, and audit logging to meet enterprise compliance requirements.

Deployment is straightforward: a single curl command installs the complete stack on any Linux server with 2 cores and 4GB of RAM. Docker, offline installer packages, and 1Panel app store integrations are also supported. A desktop edition for Windows and macOS enables personal use without server infrastructure, while a distributed mode using Nacos service discovery handles high-availability production deployments.

What You Get

• Drag-and-drop chart builder — select dimensions and metrics from connected data sources and generate 20+ chart types including bar, line, pie, scatter, heat maps, and geographic visualizations with no code required
• 200+ industry dashboard templates — pre-built layouts for manufacturing, e-commerce, finance, healthcare, and logistics that can be applied with one click and customized to specific datasets
• 20+ data source connectors — native drivers for MySQL, Oracle, SQL Server, PostgreSQL, MariaDB, TiDB, ClickHouse, Apache Doris, Apache Impala, StarRocks, Amazon Redshift, MongoDB-BI, Excel, CSV, and REST APIs
• AI natural language querying via SQLBot — type questions like ‘what were last month’s top-selling products?’ and the integrated SQLBot module generates and executes the SQL, returning results as a visual chart
• Embedded analytics SDK — three embedding modes (iframe, REST API, JavaScript SDK) let developers integrate DataEase dashboards directly into enterprise portals, CRM systems, ERP platforms, or SaaS products
• Granular sharing and access controls — role-based permissions, data field masking, password-protected public links, token-based API access, and full audit logging for regulatory compliance
• Multiple deployment targets — one-line Linux installer, Docker container, offline installation packages, 1Panel app store, and a standalone desktop application for Windows and macOS
• 12-language interface — full localization for Simplified Chinese, Traditional Chinese, English, Japanese, French, German, Spanish, Portuguese, Korean, Turkish, Indonesian, and Arabic

Common Use Cases

• Sales performance dashboards — e-commerce teams connect MySQL order databases and drag revenue, customer, and geographic fields to build monthly sales screens that previously required a BI analyst
• Financial risk monitoring — bank risk teams link Oracle data warehouses to build credit delinquency rate heat maps, customer segment scatter plots, and regional risk dashboards consumable by non-technical compliance staff
• Manufacturing floor monitoring — factory operators connect PLC data interfaces and SQL Server historians to display real-time equipment utilization, defect rates, and downtime metrics on large-format display screens
• SaaS product embedded analytics — product companies integrate the DataEase SDK to add self-service reporting modules to their customer portals, reducing churn by giving clients direct visibility into their own data
• Public sector situational displays — city transportation agencies pull API data feeds into DataEase to build live traffic status, bus ridership, and carbon emission monitoring screens shared across departments
• Education data reporting — university administration offices analyze enrollment, attendance, and grade distribution data to produce visual reports for leadership without requesting custom development from IT

Under The Hood

Architecture

DataEase follows a layered monolith pattern on the backend with a Java Spring Boot application that organizes functionality into discrete domain packages — datasource, dataset, visualization, chart, share, engine, exportCenter, and ai — each containing server, manage, dao, and dto sub-layers. This separation of concerns is intentional but enforced by convention rather than module boundaries. The frontend is a Vue 3 single-page application with Pinia for state management, organized around views, components, hooks, and stores, with Vite as the bundler. A distributed deployment mode is supported via Nacos service discovery, allowing horizontal scaling of the backend while the standalone mode runs everything in a single process. The data query path flows from a frontend chart configuration through a Spring REST API into an Apache Calcite SQL parsing layer, which translates normalized query ASTs into dialect-specific SQL for execution against the target data source.

Tech Stack

The backend runs on Java 21 with Spring Boot 3.3 and MyBatis-Plus for ORM, backed by MySQL as the application metadata store. Apache Calcite provides cross-dialect SQL parsing and function translation, while Apache SeaTunnel handles batch data synchronization jobs. The frontend is built with Vue 3, Element Plus as the UI component library, AntV G2Plot and S2 for chart rendering, ECharts for additional visualization types, and AntV L7 for geographic map layers. TinyMCE powers rich-text editing within dashboard components. Docker deployment uses a custom Alpine-based OpenJDK 21 image with pre-staged driver JARs and static resources. The SDK module exposes a Java API for building custom data source providers and chart extensions.

Code Quality

The codebase has limited automated test coverage — a small number of JUnit tests exist in the backend covering specific utilities like chart Excel export logic and datasource API parsing, but there are no comprehensive integration test suites or frontend test files. The backend uses structured exception handling through a custom DEException class and Spring’s @ControllerAdvice for global error responses. MyBatis-Plus reduces boilerplate for data access. The frontend TypeScript adoption is moderate — types and interfaces are defined in a dedicated models layer, though component files occasionally use looser typing. GitHub Actions workflows include a typos check and an LLM-based code review step, indicating emerging quality gate adoption. Inline comments are sparse in the backend domain logic but more present in complex chart configuration files.

What Makes It Unique

DataEase occupies a specific niche as a self-hosted BI tool with genuine ease-of-use for non-technical users, distinguishing it from tools like Apache Superset that require more configuration expertise. The integrated SQLBot module — a separate open-source project consumed as a dependency — brings natural language to SQL conversion directly into the dashboard workflow without requiring users to leave the interface or configure a separate AI service. The xpack module structure provides a documented extension point for enterprise capabilities including distributed coordination, additional authentication protocols like OIDC and CAS, and advanced sharing features, with the core open-source build handling these through stub implementations. The geographic visualization support using AntV L7 with bundled map files (pre-staged in the Docker image) enables offline-capable geographic dashboards without external tile server dependencies, which is significant for air-gapped enterprise deployments.

Self-Hosting

DataEase is licensed under the GNU General Public License version 3 (GPLv3), a strong copyleft license maintained by the Free Software Foundation. GPLv3 permits commercial use, modification, and redistribution, but requires that any distributed derivative works also be released under GPLv3 and include source code. For most self-hosters running DataEase internally — without distributing modified versions to external parties — the copyleft requirement does not impose practical restrictions. Organizations using DataEase solely within their own infrastructure are free to do so commercially without licensing fees or obligations to publish modifications.

Running DataEase yourself requires a Linux server with at least 2 CPU cores and 4GB of RAM as a minimum, with production workloads benefiting from more resources depending on concurrent user count and query volume. The installation packages MySQL, the Java runtime, and all required services as a unit, so the operational surface includes database backups, disk space management for stored datasets and exported files, and periodic version upgrades using the provided upgrade script. High-availability deployments require the distributed mode with Nacos for service discovery, which adds operational complexity. There is no managed upgrade process — administrators must apply new releases manually by following the project’s offline or online upgrade documentation. Logging, monitoring, and alerting integrations are the operator’s responsibility.

FIT2CLOUD, the company behind DataEase, offers a commercial edition that adds enterprise capabilities not present in the open-source build — including LDAP/AD user synchronization, SSO via OIDC and CAS, row-level data permissions, multi-tenant workspace isolation, advanced data governance features, and SLA-backed support. The community edition relies on GitHub Issues and a Discourse-style forum for support, with no response time guarantees. Organizations evaluating the commercial edition should assess whether the enterprise authentication integrations and formal support channels justify the additional cost relative to the operational maturity their team can provide internally.